code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size=3,
      stride=1,
      padding=1,
      type=1,
  ):
      super(mfm, self).__init__()
      self.out_channels = out_channels
      if type == 1:
          self.filter = nn.Conv2d(
              in_channels,
              2 * out_channels,
              kernel_size=kernel_size,
              stride=stride,
              padding=padding,
          )
      else:
          self.filter = nn.Linear(in_channels, 2 * out_channels)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import Parameter
label_desc: Use image node_id 1 to create a new mfm object from inherited base classes: nn with example: obj = mfm(in_channels, out_channels, kernel_size, stride, padding, type)